The present invention relates to process control in general, and more particularly, to a real time computer technique for the optimization and control of a process.
Optimization techniques are known to maximize the production and/or to minimize the cost of operating an industrial process, especially with regard to the consumption of energy and the correlating production output. These techniques are particularly applicable in chemical engineering, and they have been used there for establishing operating conditions that yield a maximum return on investment while minimizing operating costs.
The prior art reveals mathematical optimization techniques, such as linear programming and evolutionary operation techniques. The latter has been paralleled with four other tools used in the past toward such goals. See for instance Chemical Engineering of July 5, 1965 "Process Improvement with SIMPLEX Self-Directing Evolutionary Operation" by B. H. Carpenter and H. C. Sweeney, pp. 117-126. These four earlier approaches are: (1) random search; (2) univariate exploration; (3) factorial experimentation; and (4) steepest ascent method.
The EVOP method as originally conceived operates on several key process variables to be given such set points as will yield the best result for the industrial process in terms of production to be maximized, or of costs to be minimized, while taking into account predetermined constraints of such variables. With such process variables to be controlled, a set of initial experiments is run with chosen perturbations thereof and the results in terms of a plant performance criterion, recorded. The poorer result is connected across a line to two other runs having higher results in order to determine the level of a new and intermediary experiment. In this approach, each succession of experiments automatically leads to a region of higher results. This involves calculations conducted on the side with the assistance of a computer, or microprocessor, and the determination of a new run in the succession of runs required involves an elapse of time during which the initial conditions may have been changing. The amount of time required to achieve an optimum varies with the number of set points to be perturbed and there is, therefore, some uncertainty in the expectation of an optimum at the end of such sets of experimental runs if the EVOP technique is used to actually change the set points of the working process control loops.